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Electronic Commerce Business Models:
A Conceptual Framework
By
Utkarsh Majmudar
Ganesh N. Prabhu
July 2000
Please address all correspondence to:
Prof. Utkarsh Majmudar
Visiting Faculty (Finance & Control Area)
Indian Institute of Management
Bannerghatta Road
Bangalore - 560 076
India
Fax: (080) 6584050
E m a i l ! utkarsh&qimbxmeUn
Copies of the Working Papers may be obtained from the FPM &
Research Office
Electronic Commerce Business Models: A Conceptual
Framework

Utkarsh Majmudar and Ganesh N. Prabhu
Abstract
The recent boom in the new economy of internet based
commerce has created a large
number of firms with a variety of business models that aim to
leverage the power of the
internet to further their business goals. In this paper we attempt
to provide a
conceptual framework for understanding e-commerce business
models on a number of
important dimensions - nature of consumer activity, nature of e-
commerce activity,
target customers, targeting strategy, revenue generating modes,
procfactfcerwce
delivery modes, payment collection modes, operating modes,
market places,
advantage mechanisms and domination characteristics. We also
examine means of
improving value proposition and net-friendliness for e-
commerce activities and identify
areas where e-commerce models have not been explored or fully
exploited so far.
Since the range of economic activities on the internet is vast

and growing, newer
models and opportunities are likely to emerge through
improvements in internet
technologies as well as innovations in their application to
business contexts. Hence
any conceptual framework on e-commerce business models,
including our own, can
never be comprehensive.
Framework - 1 -
Framework
Introduction
commerce has spawned a large
leverage the power of the internet to
further their business goals. This paper provides a conceptual
framework for understanding these
business models and their characteristics. The conceptual
framework is shown in Figure 1.

Locating Electronic Commerce in the Internet Economy
We define electronic commerce as use of the internet medium
for conducting economic
transactions. Electronic commerce is a part of a larger internet
economy. Conceptually, the internet
economy can be divided into four layers (Barua etal., 1999).
Each layer of the internet economy is
listed below with descriptions of the types of companies and
names of some of the actual companies in
each category.
(a) Layer One: The Internet Infrastructure Layer. This layer
includes companies with products and
services that help create a network infrastructure, a prerequisite
for electronic commerce. The
categories in this infrastructure layer include: Internet backbone
providers (e.g., MCI); Internet
service providers (e.g. AOL); Networking hardware and
software companies (e.g., Cisco); PC and
Server manufacturers (e.g. HP); Security vendors; Fiber optics
makers; Line acceleration hardware
manufacturers (Barua, etal., 1999).
(b) Layer Two: The Internet Applications Layer: Products and
services in this layer build upon the

above IP network infrastructure and make it technologically
feasible to perform business activities
online. The categories in this applications layer include:
Internet consultants (e.g., USWeb/CKS,
Scient, etc); Internet commerce applications (e.g., Netscape);
Multimedia applications (e.g.,
RealNetworks); Web development software (e.g., Adobe);
Search engine software (e.g., Inktomi);
Online training; Web-enabled databases etc.(Barua etal., 1999).
Framework - 2 -
(c) Layer Three: The Internet Intermediary Layer: Internet
intermediaries increase the efficiency of
electronic markets by facilitating the meeting and interaction of
buyers and sellers over the Internet.
They act as catalysts in the process through which investments
in the infrastructure and
applications layers are transformed into business transactions.
The categories in this intermediary
layer include: Online brokerages (e.g., ETrade); Online travel
agents (ITN); Market makers in
vertical industries; Content aggregators (e.g., Cnet,);

Portals/Content providers (e.g., Yahoo);
Internet ad brokers (e.g., Doubleclick); Online advertisers
(Barua et.al., 1999).
(d) Layer Four: The Internet Commerce Layer Internet
commerce involves the sales of products and
services to consumers or businesses over the Internet The
categories in this Internet commerce
layer include: E-tailers (e.g., Amazon.com, eToys.com);
Manufacturers selling online (e.g., Cisco,
Dell, IBM); Fee/Subscription-based companies (e.g., WSJ.com);
Airlines selling online tickets
(Southwest.com); Online entertainment and professional
services (Barua etal., 1999).
Many companies operate at multiple layers in the internet
economy. For instance, Microsoft
and IBM are important players at the internet infrastructure,
applications, and internet commerce layers,
while AOL (before the acquisition of Netscape) is a key player
in the infrastructure, intermediary and
commerce layers. Similarly Cisco and Dell are important
players at both the infrastructure and
commerce layers. As suggested by Barua etal. (1999), even
though the four-layer internet economy
framework makes it difficult to separate revenues for multi-

layer players, it presents a more realistic
view of the internet economy than a monolithic
conceptualization that does not distinguish between the
different layers. Further, this classification helps in analyzing
how companies choose to enter one
internet layer, choosing later to extend their activities to the
other layers (Barua etal., 1999).
In this paper we treat e-commerce firms as those that
principally belong to the fourth layer, but may
include some from the third layer as well.
Framework - 3 -
Payment collection modes
• Physical
• Virtual
Revenue sources
Sale of goods/services; Agent fees,
Priced packaged information;
Advertising Revenue; Priced
information;
Sale of aggregated marketing
information
m A
Revenue

Activities
• B2B (Single/Multiple)
• B2C (Single/Multiple) <-
• C2B (Single/Multiple)
• C2C (Single/Multiple)
£_
<
Firms (Click only or click-n-mortar)
> Pure e-commerce
• e-shops,e-groceries, e-mails
> E-commerce portals
• Verticals, Horizontals, Sportals
Pro. iuct
Pricing
• Buyer dominated
• Seller dominated
• Parity relationship
Advantage mechanism
• Fixed price
• Name your price
• Buyer aggregation
• Real-time buyer seller matching
• Auctions
• Tender and Request for Proposal
• Consortia
Product Delivery

• Physical
• Virtual
Product Types
• Sale of goods/services
• Packaged marketing information
• Advertisement banners
• Priced information
• Agent Services
• Research and Development
Target Market
• Niche
• Broad
Figure 1
Framework -4-
Customer Activities in Electronic Commerce
Economic transactions over the internet consists of four major
sequential activities by the
prospective customer. The two initial activities are:
(a) Product or service search: The prospective customer
searches for the required product or service
with appropriate features either in the physical world or over
the internet. This search process can
be short and simple for products with few features and quite

long and difficult for products with
large number of features that have to be evaluated
comprehensively. Internet sites like
productorium.com offer product-feature search and comparison
services for relatively complex
consumer products that have multiple suppliers - therefore
facilitating the search and evaluation
process.
(b) Price search: After searching for the appropriate product or
service, the prospective customer
searches for price offerings on the required product or service
either in the physical world or over
the internet. This search process can be limited in scope in
cases where brand preferences are
strong, only one supplier exists, unit costs are low or the
product commands low customer
involvement for purchase. It can be extensive in scope in cases
where low or no brand preferences
exist, multiple reliable suppliers exist, unit costs are high or the
product requires high involvement
for purchase.
Product and price search can be iterative in some cases. These
are followed by:

(c) Actual Purchase: Once the appropriate product or service is
found and the price search ends
appropriately, the customer makes an actual purchase either in
the physical world or over the
internet.
(d) Payment: Once the product or service has been purchased,
the customer makes a payment (for
the goods or services received) either in the physical world or
over the internet
These sequential activities may end at the first or second point
without any economic
transaction being affected. While the internet can support the
first two activities and does so in most
Framework - 5 -
cases, we consider an economic xransacnon 10 De pan OT
electronic commerce oniy IT me actual
purchase utilises the internet.
Electronic Commerce Activities
To affect and influence one or more of the four major sequential
activities by the prospective
customer as described above, e-commerce firms act directly or

indirectly in several ways as given
below:
(a) Direct selling of goods and/or services over the internet:
This involves providing product
information and price details on the internet to enable a
customer to buy the product or service
through online or offline payment mechanisms. This is what is
commonly recognised as e-
commerce activity.
(b) Provision of packaged marketing or company information on
a company homepage: Though,
this would not classify as e-commerce but it does provide
important information about the
company's products and provoke direct or indirect enquiries and
subsequent purchase.
(c) Advertising banners on popular internet sites for publicity
and marketing: Communicates
packaged information regarding the company advertised and
provides links to the company's
homepage. The host company may also earn some revenue by
providing advertising space. This is
not a true form of e-commerce but adds a revenue generating
activity.

(d) Priced information: Firms may provide priced information
regarding products or services or
provide comparative information that may be of value to niche
customers. In some cases, the
information may be provided free to end-customers as the
featured firms/products pay for the
service.
(e) Agent services: Firms may act as internet based agents and
provide an internet site that helps in
bringing the buyer and the seller together and therefore earn
commission income.
Framework - 6 -
(f) Research and development: Firms may gather product
preference and use information through
their own sites or through popular general sites
(doubleclick.com) through registration information
or through contests and use the information to provided targeted
products. This supports e-
commerce activity.
Targeting Prospective Customers in E-Commerce
The target customer segment for the firm could be either niche

or broad as given below:
(a) Niche: Involves targeting a niche single customer or a group
of customers within a population of
appropriate customers. These are typically targeted through
information on specialised sites or
through registration information gathered from general or
specialised site or through search engine
key words entered by the prospective customer.
(b) Broad: Involves targeting the entire population of
appropriate customers, typically through
information presented on general or specialised sites depending
on the product.
Targeting Strategy in E-Commerce
The targeting strategy used by the firm could be either based on
providing:
(a) Cost advantage in terms of overall transaction and lifetime
costs over other products available in
either physical stores or over the internet or both. Cost
leadership in the physical world involves
becoming a low cost producer through gaining experience,
investing in large scale production
facilities, using economies of scale and carefully monitored
operating costs (Porter 1985). While

these hold in e-commerce also, cost leadership positions can be
enhanced over the internet by
reducing transaction costs through appropriate use of internet
capabilities.
(b) Differentiation in terms of features and/or services.
Differentiation in the physical world involves
development of unique products or services and building on
brand or customer loyalty (Porter
1985). While these hold in e-commerce also, differentiation
positions can be enhanced over the
Framework - 7 -
internet by providing value added services and customisation
features through appropriate use of
internet capabilities.
Firms may pursue different e-commerce approaches depending
on whether they are targeting
a niche customer segment or a broad customer segment and
whether they are adopting a
differentiation strategy or a cost leadership strategy. The
options are shown in Table 1 along with
appropriate examples.

Table 1
Niche Customer Segment
Broad Customer Segment
Internet Based Differentiation
Focus Internet Differentiation
Eg. Jbmountainbikes.com
Broad Internet Differentiation
Eg. Oldtools.com
Internet Based Cost Leadership
Focus Internet Cost Leadership
Eg. southwest.com
Broad Internet Cost Leadership
Eg. amazon.com
E-Commerce Revenue Generating Modes
The nature of electronic commerce activity also dictates the
revenue generation modes that
can be adopted by the firm. Apart from revenue generation
modes adopted in the physical world, firms
can develop new revenue generating modes through the
appropriate use of internet capabilities.
Some revenue generating modes adopted by e-commerce firms
are:

(a) Profit from purchase/sale of goods: These are typically in
the nature of trading profits as existing
in the physical world though the reach of the internet can
enhance its quantum.
(b) Agent fees: Income generated by an agent for bringing the
buyer and the seller together. This is
usually charged from the seller, though in some cases both
parties may be charged. This is as it
exists in the physical world though the reach of the internet can
enhance its quantum.
(c) Income from priced packaged information: Revenue
generated from the sale of information that
is rendered more valuable to the consumer as it is appropriately
collected, classified and in an
easily searchable form. This is as it exists in the physical world
though internet capabilities can
provide greater currency to the information.
Framework - 8 -
(d) Advertising revenues: Revenues generated through
advertisements put as images or scrollbars
or pop-ups on popular internet sites. Revenues may be
calculated based on "page-views" (number

of people visiting the page where the advertisement is
displayed) or "dick-throughs" (number of
people clicking the advertisement image to visit the sponsors
web-site). Revenues are based on
measures similar to those that apply to physical hoardings or
newspaper/magazine advertisements.
However, internet capabilities can provide more accurate
measures of viewership in terms of
number and quality than possible for physical hoardings or
newspaper/magazine advertisements.
(g) Priced information: Income generated from providing priced
information regarding products or
services or providing comparative information that may be of
value to niche customers. These differ
from agent commissions, though in some cases, the information
may be provided free to end
customers as the featured firms/products pay for the service.
(h) Income from sale of aggregated marketing information:
Firms may like to sell aggregated traffic
and customer data to marketing research firms. This is arevenue
source for firms.
E-Commerce Product Delivery Modes
There could be two types of product delivery modes in

electronic commerce activity:
(a) Physical: Delivery through physical shops or delivery
through delivery services providers such as
courier services. The transaction ends with safe delivery except
in cases where the physical
product requires maintenance, repairs, replacements and other
types of follow-up activity. In the
latter cases, the follow-up can be provided either by local
agents of the firm or by the firm itself.
Such follow-up can be either through visits or email or a
combination of the two.
(b) Virtual: Typically used for information products (reports,
music, software etc). After payments have
been made, the product may be made available for viewing on
the screen or for direct download or
through e-mail delivery. Any follow-up required is usually
through email.
Framework - 9 -
E-Commerce Payment Collection Modes
There could be two types of payment collection modes in
electronic commerce activity:

(a) Physical: Payments collected through agents from physical
shops or against delivery through
delivery services providers such as courier services.
(b) Virtual: Payments collected through credit cards, charge
cards, smart cards, gift cheques, e-cash
or frequent flier points.
E-Commerce Operating Modes
There can be three mayor modes of handling electronic
commerce business operations:
(a) Completely outsourced operations: Activities like web-
hosting, back-end processing and order
fulfilment are outsourced to other specialised firms or a
consortia. The firm that builds the product
chooses to concentrate on its physical world operations or on
manufacturing alone.
(b) Completely self-handled operations: All e-commerce related
activities like web-hosting, back-end
processing and order fulfilment activities are handled in-house
in parallel with its physical world
operations and manufacturing.
(c) Hybrid operations: Part of the e-commerce activities
(typically back-end processing and order
fulfilment) are handled by the firm in parallel with its physical

world operations and manufacturing,
while the rest (typically web-hosting) are outsourced to
specialised firms or to consortia.
E-Commerce Market Places
The e-commerce market place (where buying and selling occurs)
could take the shape of:
(a) Pure E-Commerce Shops: Like physical shops, pure e-
commerce shops are places where only
product and price information is provided and the purchase
transaction is conducted. These are of
three types: (1) e-shops, (2) e-groceries and (3) e-mails.
(1) e-shops: a single shop typically selling a single type of
product or service on a single site.
Framework -10 -
(2) e-groceries: a single shop typically selling multiple types of
related products or services on a
single site.
(3) e-mails: a set of multiple shops selling a variety of products
and/or services that are hosted by
a single mall site. The mall may have a single owner/manager or
multiple owners/managers.

Some malls may provide for order and/or payment consolidation
across shops within the mall,
while others may need the customer to place individual orders
or make individual payments for
each shop in the mall. Malls typically have a theme that decides
the types of shops that are
included in it. The theme may be based on geographical location
(eg. an Indian mall) or based
on the type of goods sold (eg. a clothes mall) or a combination
of both.
(b) E-Commerce Portals: Unlike typical physical shops, these
are places where apart from providing
product and price information and conducting the purchase
transaction, additional information and
links to other related sites are provided to the customer to aid
and influence the purchase decision.
A portal also provides useful related or unrelated information to
a potentially non-transacting
customer - such as specialised repair and care information, new
product or technology
developments etc. Apart from revenues from the purchase and
sale of goods and services, popular
portals also draw commission revenues or obtain discounts from
product manufacturers and

providers of related services for providing them visibility.
Portals can be (1) vertical (vortals) or (2)
horizontal (hortal) or (3) spot (sportal).
(1) Vertical portal: It typically showcases a single product
category and provides information on
multiple brands of that products as well as closely related
product information. It typically caters
to a subset of the audience that is interested in that product
alone either as a buyer, user or
seller and will seek out the portal to meet its needs. Vertical
portals can draw commission
revenues or discounts due to the targeting they provide.
(2) Horizontal portal: It typically carries a range of largely
unrelated product categories and
provides information on multiple brands of the set of products
that it chooses to carry as well as
Framework -11 -
a host of related product information as well as unrelated or
loosely related information and
services. It typically attempts to attract a large and wide variety
of customers to its site by

providing a broad range of products and services in the hope
that some visiting customers may
purchase from the site. Horizontal portals can draw commission
revenues or discounts due to
the visibility they provide.
(3) Spot portal: It attempts to combine the advantages of the
horizontal and vertical portals (eg.
computor.com). It typically carries a range of related product
categories and provides
information on multiple brands of the set of products that it
chooses to carry as well as a host of
related product information and services. Therefore it is broader
in the range of products it
carries than a vortal and provides more products and product
information in each product
category than a hortal. It typically attempts to attract a fairly
large but targeted customer
segment to its site that is likely to buy the type of related
products that it features. Spot portals
can draw commission revenues or discounts due to the
combination of targeting and visibility
they provide.
Table 2 depicts the types of shops and portals that are possible.

Table 2
E Commerce Shops
E Commerce Portals
Single Product
E-Shop
Vertical Portal
Multiple Related Products
E-Grocery
Spot Portal
Multiple Unrelated Products
E-Mail
Horizontal Portal
Matrix of E-Commerce Models
Two principal business models - commonly known as B28
(business-to-business) and B2C
(business-to-consumer) proliferate in the e-commerce market
space today. The reverse variants like
C2B (consumer-to-business) and C2C (consumer-to-consumer)
are emerging but less commonly
adopted. However, buyers in the e-commerce market space can
be either single or multiple individual
consumers or single or multiple businesses. Similarly sellers in
the e-commerce market space can be
either single or multiple individual consumers or single or

multiple businesses. (Consumer sellers are
Framework -12 -
individuals interested in selling used products second hand or in
selling partially used or unused
services that they are unable to utilise fully.) Therefore the
buyer-seller space in e-commerce could also
be viewed more comprehensively as shown in Table 3.
Table 3
Business Seller
Consumer Seller
Single
Multiple
Single
Multiple
Business Buyer
Single
SB to SB
MB to SB
SCtoSB
MCtoSB
Multiple
M6MMB

SCtoMB
MCtoMB
Consumer Buyer
Single
SBtoSC
MBtoSC
SCtoSC
MCtoSC
Multiple
mmm
MBtoMC
SCtoMC
MCtoMC
Abbreviations: S: Single; M: Multiple; B: Business; C:
Consumer
Each of these are combinations that can provide potentially
viable business models. However
the five models that are highlighted in the chart seem to be the
most commonly adopted models. The
rest are either uncommon or have not been adopted so far -
providing areas for exploration and
exploitation by e-commerce firms in future.
Deriving Advantage through E-Commerce Business Models
Within each principal business model one could have
mechanisms to derive advantage for the
business or the customer through the nature of delivery,

customisation, seller characteristics, price-
setting mechanism etc. Therefore one could conceive the matrix
in Table 4 for conceptualising the
business model configuration (examples are provided for each
of the models wherever available).
Let us see as to how each market place works:
(a) Fixed price model: The seller offers a list of goods available
at pre-determined prices. The
consumer's choice is limited to take it or leave it.
(b) Name your price: Different products are put out for sale.
The consumer sets the price that he is
willing to pay. Net agents' scout for the seller who is willing to
sell at the price quoted by the
consumer. The sale is fixed and the consumer is required to buy
the product.
Framework - 1 3 -
(c) Buyer Aggregation: This operates on the basis of quantity
discounts. The net agent collates
orders from a large number of customers. As soon as the order
level touches a pre-specified level,
the sale is triggered and the benefit of quantity discounts passed

on to the consumer.
(d) Real time buyer-seller matching: A real-time exchange
brings name brand retailers, small stores
and individual sellers in a single market where one can instantly
compare total prices (including tax
& shipping). If the customer doesn't like the prices that he sees,
he can ask the sellers to give the
customer a better deal.
(e) Auctions: They are similar to auctions in the real world.
Sellers place items for sale and may quote
a reserve (minimum) price. Buyers bid for the items and
(typically) the highest bidder gets the item
as long as his bid is higher than the reserve price.
(f) Tender and Request for Proposal process: Typically used by
government agencies for inviting
tenders for buying and selling of items.
(g) Consortia: This is a variant of buyer aggregation that is
typically found in the B2B segment. A
number of businesses with similar buying requirements get
together for buying at a common place.
On the other hand, there could be a large number of sellers
willing to sell to a common pool of
buyers.

Table 4
Advantage Mechanism
Fixed Price Model
Name Your Price
Buyer Aggregation
Real Time Matching
Auctions
Tender RFP Processes
Consortia
Basic Business Model
B2B
Cisco, Intel
Freemarkets.com
Ec21.nbc.gov
e-steel.com
B2C
Amul.com
Priceline.com
Buyasone.com
Nextag.com
e-bay.com
na
e-steel.com
C2B
na
Priceline.com

na
na
C2C
na
na
na
e-bay.com
na
na
na: not applicable
Framework - 1 4 -
Domination in E-Commerce Markets
E-commerce markets can be designed in ways that are
dominated by either the buyer or the
seller in terms of who sets the price or who dominates in the
price setting negotiation (if any). In a few
markets neither party dominates and there is a parity
relationship. The price setting process could be
static (one price for all customers) or dynamic (prices change
depending on the buyer and/or the time of
sale).
The nature and implications of the three market domination

categories are given below:
(a) Buyer dominated: Here the buyer has the upper hand in the
transaction. These include Name
Your Price, Buyer Aggregation, Tender and Request for
Proposal Process, Auctions and Consortia.
(b) Seller dominated: Here the seller has the upper hand in the
transaction. Fixed Price models fits
into this category.
(c) Parity relationship: Here neither the buyer nor the seller
dominates the transaction. Real Time
Buyer-Seller matching fits in this category.
Click-Only or Click-n-Mortar E-Commerce Firms
There are two broad types of firms engaged in electronic
commerce: pure plays (click-only) and
hybrids (click-fi-mortar). Pure plays are firms that do not have a
physical presence (like Amazon.com;
which sells on the net only and does not own any physical
bookstores). While others have a physical as
well as an internet presence (like BarnesandNoble.com, which
has a large network of retail bookshops
and sells on the net too). Both pure plays as well as hybrids
engage in a wide variety of businesses viz.
travel, books, software, consumer electronics, groceries etc.

Factors Affecting Potential Sale in E-Commerce
There are several factors that can affect the potential sale of any
product/ service over the
internet.
Framework -15 -
We list some of the factors or variables that can affect the
potential sale of any product/service
over the net (partly based on Rajiv and Saxena, 2000):
(a) Value for money: Does the sale of the product over the net
provide additional benefit to the
consumer? Is the benefit purely monetary in terms of price
reductions or is the monetary benefit (if
any) enhanced by ease of transaction or improved product
delivery.
(b) Commodity Product/Service: Lesser the difference between
products, the easier the choice
process for the consumer over the net. Fewer comparisons have
to be made - price becomes the
major differentiating factor.
(c) Brand image: Products/services with a strong brand appeal

are more likely to succeed over the
net, especially for otherwise commodity products. They also
reduce search costs over the net.
(d) Support services: Products/services that require lesser
amount of support (order tracking,
warranties, after-sales, call centres etc.) are more likely to
succeed over the net. Bse a delivery
mechanism for support services needs to be built in along with
the sale transaction mechanism.
(e) Sensory data processing: Products that entail greater sensory
involvement of the consumer are
less likely to succeed as the product cannot be sensed
effectively. Else a sensory delivery
mechanism for sensory involvement of the consumer needs to be
built in along with the sale
transaction mechanism.
(f) Consumer choice: Modes of selling that enable a consumer
to make better choices (through
product category or brand comparisons) will improve the
success of the product over the net
(g) Payment mechanisms: Ease of payment and security of
payment mechanisms are concerns for
potential customers. Ceteris paribus, barter and coupon schemes
can enhance sales over the net.

(h) Personalisation: The more customer-friendly and
personalised the interface (customers can define
which related sites they wish to visit or what information they
would like to be provided when they
log in); greater is the likelihood that customers would be
attracted to and attached to the site.
Framework - 1 6 -
(i) Seamless integration: Various stores on the site and various
functionalities need to be tightly
integrated (for example timely update of prices), to provide the
consumer with the ease of
transaction that would attract and attach the customer.
(j) Transaction costs: Any lowering of transaction cost for
customers will attract them to the site. This
could take the form of using a single site to conduct
transactions over multiple sites (snaz.com) or
reduce the customer's efforts in going to a physical store to
ensure quality of products.
(k) Unit cost: Lower the per unit cost of the goods or service,
lower the propensity to transact over the
net if physical purchase transactions are fast, easy and cheap.

An internet transaction, however
small and simple requires a minimum fixed investment in terms
of time for search and time for
transaction as well as the cost of internet connectivity that may
not justify purchases of small unit
cost except along with other high unit cost items.
(I) Frequency of purchase: Higher the frequency of purchase of
the goods or service, lower the
propensity to transact over the net if physical purchase
transactions are fast, easy and cheap. An
internet transaction, however small and simple requires a
minimum fixed investment in terms of
time for search and time for transaction as well as the cost of
internet connectivity that may not
justify frequent and repeated purchases that can be handled
through rate contracts and regular
door deliveries.
Enhancing Product/Service Value Propositions through E-
Commerce
Typically value offerings in an e-commerce transaction are
derived from providing the
consumer with products/services which provide greater benefit
to the consumer through the transaction

over the net as compared to the purely physical transaction.
This enhanced value offering can be due to:
(a) reducing the physical space between the buyer and the
seller;
(b) reaping economies of a shortened value chain through
disintermediation;
Framework - 1 7 -
(c) reaping economies derived from a large global consumer
base; and
(d) provision of easily accessible related information through
the net.
Apart from providing purchase access to customers unable to
undertake physical transactions,
where these advantages are relatively large in proportion to the
value of the product, it improves the
value proposition for the buyer while maintaining or enhancing
margins for the seller. Ceteris paribus,
an improvement in the value proposition of the product through
its transaction over the net can
potentially improve the propensity of customers to purchase it
over the net. This assumes that the

accessibility, transaction cost and level of difficulty for both
types of transactions remains the same.
This is however mediated by the inherent net-friendliness of the
product as described in the next
section.
Impact of Net-Friendliness of Product/Service in E-Commerce
Not all products/services lend themselves to an enhanced value
offering through transactions
over the net. In some products/services, difficulties related to
the nature of the product or the nature of
the transaction may reduce the value offering on a transaction
over the net - thus reducing the
propensity of the consumer to use the net for transactions of
such products. Difficulties that are related
to the nature of the product/service itself are said to be due to
the inherent net friendliness of the
product (Rajiv and Saxena, 2000). For example, in a fabric
purchase context, the customer normally
feels the texture of the fabric before purchase and therefore will
be reluctant to purchase it over the net
- this renders fabrics as an inherently non-net-friendly product.
Therefore, we could consider a
continuum of products/services from completely non-net-
friendly to highly net-friendly ones depending

on how important the net-friendly or non-net-friendly feature is
to the purchase decision of the
customer.
However, certain value-added services and mechanisms can
shift non-net-friendly
products/services to a higher level of net-friendliness. To
develop such value-added services and
Framework -18 -
mechanisms firms need to understand the features that are likely
to influence a product/service's
degree of net-friendliness.
Value-Proposition - Net-Friendliness Matrix
If we combine the degree of net-friendliness as a continuum
with the level of improvement in
the value proposition of transactions over the internet as a
continuum we have the e-commerce market
space given in Figure 2. It indicates that certain products and
services are inherently more appropriate
for e-commerce transactions. Firms can develop mechanisms to
enhance value propositions and/or

net-friendliness at a cost - such costs can be marginal in some
products or prohibitive in others
impacting the overall e-commerce business proposition.
Figure 2
Non Net Friendly <- - • Highly Net Friendly
Low Value Proposition Improvement Worst Net Product (avoid)
High Value Proposition Improvement Needs Net Friendliness
Support
Good for Click & Mortar
Best Net Product
Mechanisms to Improve Net Friendliness:
Several mechanisms have been developed to improve the net-
friendliness of inherently non-
net-friendly products.
We present and explain some of the mechanisms adopted by the
existing e-commerce firms:
(a) Sending samples by mail/courier: Example: Sending a
sample of small pieces of fabric to the
customer by mail enables the customer to feel the texture prior
to purchase.
(b) Better Computer Imaging: Example: Car sites use high
quality three dimensional imaging with

multiple perspectives and "walk-arounds" to provide the
customer an almost live model appearance
on screen prior to purchase.
Framework -19-
(c) Monitor resolution and calibration: Example: Paint sites can
accurately calibrate the image sent
against the user's stated monitor type, model and settings so that
the customer can view authentic
undistorted 'real" colours prior to purchase.
(d) User demonstration: Example: Demonstration of use on
screen or video where user training is
essential as part of the sale process - as in household
appliances.
(e) Onscreen trials: Example: Viewers can try combinations of
furniture sets and relative placements
on a virtual drawing room to make suitability assessments prior
to purchase.
(f) Product details and comparisons: Example: In complex
multiple feature products, sites like
productorium.com provide easy selection and comparison
databases and navigation tools to

facilitate the customers product selection and suitability
matching process
Mechanisms to Improve Value Proposition over the Net:
Several mechanisms have been developed by firms to improve
the value proposition of the
products they sell over the internet. These mechanisms are
conceptually of three types:
(a) those that are based on value propositions that can be
offered in physical stores - these include
discounts, coupons, gift cheques, games, quizzes, contests,
demonstrations etc.
(b) those that harness the capabilities of the internet to offer
value propositions that cannot be offered
in physical stores - these include wide or targeted search
facilities, wide-net internet based
auctions, electronic cash transactions, instant email alerts etc.
(c) those based on product customisation - these involve
products purchased directly from the original
manufacturers over the net that are customised at the time of
manufacture within a limited set of
customer feature options and combinations without any major
additional cost to a manufacturer
who is geared for flexible manufacturing. This can be an

advantage over the limited range available
in physical stores.
Framework - 20 -
Conclusion
commerce has spawned a large
leverage the power of the internet to
further their business goals. Forrester Research has shown that
e-commerce could generate $6.9
trillion in revenues by 2003 (Forrester Research in ABC News,
2000).
In this paper we have attempted to provide a conceptual
framework for understanding e-
commerce business models on a number of important
dimensions like nature of consumer and e-
commerce activity, target customers, targeting strategy, revenue
generating modes, product/service
delivery modes, payment collection modes, operating modes,
market places, advantage mechanisms
and domination characteristics. We have examined means of

improving value proposition and net-
friendliness for e-commerce activities. We have also identified
areas where e-commerce models have
not been explored or fully exploited so far. Since the range of
economic activities on the internet is vast
and growing, newer models and opportunities are likely to
emerge through improvements in internet
technologies as well as innovations in their application to
business contexts. Hence any conceptual
framework on e-commerce business models, including our own,
can never be comprehensive.
References
Porter, Michael. Competitive Advantage: Creating and
Sustaining Superior Performance, The Free
Press, 1985.
Rajiv R and Saxena, Shailendra. E-Commerce; Emerging
Imperatives for Indian Businesses,
Contemporary Concerns Study, Indian Institute of Management,
Bangalore, 2000.
Tas, Jeroen. eBusines Transformation, Presentation, Mphasis-
BFL,
http://www.mphasis.com/jeroenpaper.pdf, 2000.
Anitesh Barua, Jon Pinnell, Jay Shutter, and Andrew B.
Whinston. Measuring the Internet Economy,
Center for Research in Electronic Commerce, The University of
Texas at Austin,

http://crec.bus.utexas.edu, 1999.
Forrester Research
http://more.abcnews.qo.com/$ection$/bu$iness/dailvnews/intem
etiob$000606.htmlt 2000.
Framework - 21 -
Pavlou & Fygenson/Extending the TPB
MIS Quarterly Vol. 30 No. 1, pp. 115-143/March 2006 115
RESEARCH ARTICLE
UNDERSTANDING AND PREDICTING ELECTRONIC
COMMERCE ADOPTION: AN EXTENSION OF
THE THEORY OF PLANNED BEHAVIOR1
By: Paul A. Pavlou
Anderson Graduate School of
Management
University of California, Riverside
Riverside, CA 92521
U.S.A.
[email protected]
Mendel Fygenson
Marshall School of Business
University of Southern California
Los Angeles, CA 90089

U.S.A.
[email protected]
Abstract
This paper extends Ajzen’s (1991) theory of planned behavior
(TPB)
to explain and predict the process of e-commerce adoption by
consumers. The process is captured through two online
consumer
behaviors: (1) getting information and (2) purchasing a product
from a Web vendor. First, we simultaneously model the
association
between these two contingent online behaviors and their
respective
intentions by appealing to consumer behavior theories and the
theory of implementation intentions, respectively. Second,
following TPB, we derive for each behavior its intention,
attitude,
subjective norm, and perceived behavioral control (PBC).
Third, we
1Ritu Agarwal was the accepting senior editor for this paper.
Elena
Karahanna was the associate editor. D. Harrison McKnight,
Jonathan
Palmer, and David Gefen served as reviewers.
elicit and test a comprehensive set of salient beliefs for each
behavior.
A longitudinal study with online consumers supports the
proposed
e-commerce adoption model, validating the predictive power of
TPB
and the proposed conceptualization of PBC as a higher-order
factor

formed by self-efficacy and controllability. Our findings stress
the
importance of trust and technology adoption variables
(perceived
usefulness and ease of use) as salient beliefs for predicting e-
commerce adoption, justifying the integration of trust and tech-
nology adoption variables within the TPB framework. In
addition,
technological characteristics (download delay, Website
navigability,
and information protection), consumer skills, time and monetary
resources, and product characteristics (product diagnosticity and
product value) add to the explanatory and predictive power of
our
model. Implications for Information Systems, e-commerce,
TPB, and
the study of trust are discussed.
Keywords: Theory of planned behavior, perceived behavioral
control, self-efficacy, controllability, technology adoption,
tech-
nology acceptance model, trust, electronic commerce, consumer
behavior
Introduction
Business-to-consumer (B2C) e-commerce is the activity in
which
consumers get information and purchase products using Internet
technology (Olson and Olson 2000). The potential benefits of
e-
commerce have been widely touted (e.g., Gefen et al. 2003).
However, for these information technology-enabled benefits to
materialize, consumers must first adopt online activities, such
as
getting information and purchasing products from commercial
websites. B2C e-commerce adoption—the consumer’s

engagement
116 MIS Quarterly Vol. 30 No. 1/March 2006
in online exchange relationships with Web vendors—goes
beyond
the realm of traditional marketing, and it must thus be
understood
from the viewpoint that online consumers are simultaneously IT
users (Koufaris 2002). According to Taylor and Todd (1995b),
IT
usage encompasses not only use of hardware and software, but
also
the services that surround the IT and the people and procedures
that
support its use. B2C e-commerce thus presents a unique
opportunity
to examine a user’s interaction with a complex IT system.
E-commerce adoption is an instance of IT acceptance and use
within
a setting that combines technology adoption with marketing
elements, and it thus requires distinct theorization within the
infor-
mation systems literature. However, despite an emerging
interest
among IS researchers toward the B2C e-commerce phenomenon,
there is only a limited and fragmented understanding of online
consumer behavior. The purpose of this study is to theoretically
propose and empirically test a set of factors that integrate

technology adoption with marketing and economic variables to
enhance our understanding of online consumer behavior.
B2C e-commerce has some notable differences compared to
traditional consumer behavior. First, the spatial and temporal
separation between consumers and Web vendors increases fears
of
seller opportunism due to product and identity uncertainty (Ba
and
Pavlou 2002). Second, personal information can be easily
collected,
processed, and exploited by multiple parties not directly linked
to
the transaction. Third, consumers must actively engage in
extensive
IT use when interacting with a vendor’s website, which has
become
the store itself (Koufaris 2002). Fourth, there are concerns
about the
reliability of the open Internet infrastructure that Web vendors
employ to interface with consumers (Rose et al. 1999). These
differences stress the uncertainty of the online environment and
emphasize the importance of consumer trust and the
significance of
IT adoption. More importantly, they reduce consumers’
perception
of control, confidence, and effortlessness over online activities,
creating a barrier to e-commerce adoption. Therefore,
compared to
traditional consumer behavior, perceived behavioral control
(PBC),
as described in the theory of planned behavior (TPB) (Ajzen
1991),
is likely to play a critical role in B2C e-commerce.
TPB is a well-researched model that has been shown to predict

behavior across a variety of settings. As a general model, it is
designed to explain most human behaviors (Ajzen 1991).
Hence, it
is reasonable to expect that a TPB-based model could
effectively
explain online consumer behavior. We thus create an extended
version of TPB to predict two prevalent online behaviors:
getting
information and purchasing products from Web vendors. This
study
aims to predict these two behaviors by examining the major
constructs of TPB (attitude and PBC) and their most important
antecedents. This results in a comprehensive, yet parsimonious
model that attests to the influential role of PBC, while
identifying
and validating important factors that are consistent with the
TPB
nomological structure. Moreover, the derived model explains a
substantial portion of the variance in e-commerce adoption. In
summary, this study provides conceptual clarity and empirical
validation on the following issues:
1. Adoption of B2C e-commerce is not viewed as a monolithic
behavior, but is rather proposed of both purchasing and getting
information. Since TPB has not been used to simultaneously
predict related behaviors, by modeling these two online
behaviors, we theoretically extend TPB.
2. PBC is a key determinant of both focal e-commerce
behaviors.
To the best of our knowledge, most e-commerce studies do not
account for PBC (e.g., George 2002), nor has a set of
antecedents of PBC ever been theoretically advanced or
empirically examined.
3. PBC is viewed as a two-dimensional construct formed by two

underlying dimensions (self-efficacy and controllability),
allowing a more detailed examination of external control
beliefs.
4. Trust is viewed as an antecedent of both attitude and PBC,
and
thereby integrated within the proposed TPB model.
5. Most factors are empirically shown to be IT-related (e.g.,
usefulness, ease of use, information protection), or within the
IS domain (e.g., trust, navigability), highlighting the key role
of IT in online consumer behavior.
The paper proceeds as follows: the next section discusses the
two
e-commerce behaviors, describes the TPB framework and the
nature
and role of PBC, and links TPB perceptions with intentions and
behaviors. The following section proposes and describes the
elicited
external beliefs and justifies how they link to TPB. The next
two
sections present the research methodology and results. The
final
section discusses the study’s findings, contribution, and
implications.
Electronic Commerce Adoption
Description of Online
Consumer Behaviors
Electronic commerce adoption is broadly described as the
consumer’s engagement in online exchange relationships with
Web
vendors. From a consumer behavior standpoint, getting product

information and purchasing products are generally viewed
(among
other activities) as the two key online consumer behaviors
(Gefen
and Straub 2000). While most e-commerce studies have largely
focused on product purchasing, online consumer behavior is not
monolithic since consumers must first engage in getting product
information before purchasing. Choudhury et al. (2001) argue
that
consumers do not make a single, inclusive decision, but they
rather
consider two distinct stages: getting product information and
then
purchasing the product. Gefen and Straub (2000) also
distinguish
between the two behaviors by arguing that getting information
is an
activity intrinsic to the IT since the Web system itself presents
the
MIS Quarterly Vol. 30 No. 1/March 2006 117
product information. Product purchasing, on the other hand, is
a
task extrinsic to the IT since the Web system primarily provides
the
means to achieve the purchase.
Getting information involves the transfer of information from
the
Web vendor to the consumer through browsing the vendor’s
website.

Getting information has been referred to as browsing or
window-
shopping (Gefen 2002). The value of online information search
has
been widely acknowledged (Bellman et al. 1999) since it is
critical
for learning about product specifications and potential
alternatives,
determining requirements, and gaining sufficient knowledge to
make
well-informed decisions (Choudhury et al. 2001). Product pur-
chasing refers to the procurement of a product by providing
monetary information in exchange for the focal good. In
addition to
monetary information, product purchasing usually involves
providing consumer information (e.g., address information,
product
preferences).2
These two behaviors, getting information and product
purchasing,
constitute the major part of long-held consumer behavior
models.
Engel et al. (1973) describe a five-stage buyer decision-making
process that includes problem recognition, information search,
evaluation of alternatives, purchase decision, and post-purchase
behavior. Information search corresponds to getting
information and
purchase decision to product purchasing. Ives and Learmonth
(1984) propose the customer resource life cycle (CRLF) with
three
key stages: prepurchase, during purchase, and post-purchase.
Getting information is a prepurchase activity, while product
purchasing corresponds to during purchase activities. Similarly,
Kalakota and Whinston (1997) introduce the consumer
mercantile

model (CMM) that consists of three phases: prepurchase
interaction,
purchase, and post-purchase interactions. Prepurchase
interaction
consists of product search, while comparison-shopping
corresponds
to getting information. Choudhury et al. (2001) describe four
transaction stages: requirements determination, vendor
selection,
purchase, and after-sales service. Getting information
corresponds
to requirements determination, and product purchasing to
purchase.
In sum, we focus on two behaviors—getting information and
product purchasing—that largely determine e-commerce
adoption.3
The Theory of Planned Behavior
TPB (Figure 1) is an extension of the theory of reasoned action
(TRA) (Ajzen and Fishbein 1980). TPB has been one of the
most
influential theories in explaining and predicting behavior, and it
has
been shown to predict a wide range of behaviors (Sheppard et
al.
1988).
According to TRA, the proximal determinant of a behavior is a
behavioral intention, which, in turn, is determined by attitude
(A)
and subjective norm (SN). Attitude captures a person’s overall
evaluation of performing the behavior; SN refers to the person’s
perception of the expectations of important others about the
specific
behavior. Finally, the antecedents of attitude and SN are a set

of
underlying attitudinal (bi) and normative beliefs (ni),
respectively.
Attitudinal beliefs are assessments about the likelihood of the
behavior’s consequences; normative beliefs are assessments
about
what important others might think of the behavior. Attitude and
SN
are described via an expectancy-value formula:
A ∝∑b i ⋅ ei (1)
SN ∝∑ni ⋅ mi (2)
Where: ei is the person’s subjective evaluation of the
desirability of the outcome, and
mi is the person’s motivation to comply with important
others.
Recognizing that most human behaviors are subject to obstacles,
Ajzen (1991) introduced TPB, which generalizes TRA by adding
a
third perception: perceived behavioral control (PBC). A set of
control beliefs (ci) and their perceived power (pi) (to facilitate
or
inhibit the performance of a behavior) determine PBC through
an
expectancy-value formula:
PBC ∝∑ci ⋅ pi (3)
Behavioral Intentions and
Actual Behavior
Behavioral intentions are motivational factors that capture how

hard
people are willing to try to perform a behavior (Ajzen 1991).
TPB
suggests that behavioral intention is the most influential
predictor of
behavior; after all, a person does what she intends to do. In a
meta-
analysis of 87 studies, an average correlation of .53 was
reported
between intentions and behavior (Sheppard et al. 1988).
Following
TPB, we expect a positive relationship for our two focal
behaviors—getting information and purchasing—and their
respec-
tive intentions.
2The purchasing process may be supplemented by automatic
information
extraction through cookies and data mining tools. However, it
is beyond the
scope of this study to account for this type of information
sharing, which is
not related to consumer behavior.
3We recognize the existence of other e-commerce activities,
such as
fulfillment and repeat buying. Yet, fulfillment is a vendor’s
behavior
(Kalakota and Whinston 1997). Even if post-purchase
experience influences
future behaviors, for predicting a specific behavior, the
proposed TPB
variables are supposed to take into account all previous
experiences (Ajzen
1991). Most important, consumer post-purchase behavior is
contingent upon

fulfillment, which cannot be predicted before purchase.
Attitudinal
Beliefs
Normative
Beliefs
Attitude
Subjective
Norm
Perceived
Behavioral
Control
Intention Behavior
Control
Beliefs
Attitudinal
Beliefs
Normative
Beliefs
Attitude

Subjective
Norm
Perceived
Behavioral
Control
Intention Behavior
Control
Beliefs
Figure 1. The Theory of Planned Behavior (Adapted from
Ajzen 1991)
Connecting Getting Information
and Product Purchasing
In the social psychology literature, many researchers model
related
behaviors using the TPB framework, but these behaviors are
always
modeled independently, without any attempt to capture the
extent of
their relationship (e.g., Povey et al. 2000). This raises
important
questions: Can two related behaviors be modeled
simultaneously
within the TPB framework? If so, how? Through which TPB
constructs should two related behaviors be connected? In
principle,
TPB only applies at one level of specificity. How to relate one
behavior to another remains a crucial open question (personal
communication with I. Ajzen 2003).

To explain the relationship between the two focal behaviors, we
draw upon three different aspects of consumer behavior. First,
product purchasing is contingent upon getting information.
This
notion is captured in the buyer’s decision making model (Engel
et
al. 1973), the CRLF (Ives and Learmonth 1984), and the CMM
(Kalakota and Whinston 1997), which assume a sequential
relationship between getting information and purchasing.
Second,
getting information facilitates purchases. For example, Kim
and
Benbasat (2003) argue that consumers engage in getting
information
to reduce the uncertainty of product purchasing. Third, getting
information influences purchasing. This is captured in the
theory of
mere exposure (Zajonc 1968), which holds that the frequency of
exposure facilitates a behavior. Empirical studies (Choudhury
et al.
2001; Gefen 2002) report a positive correlation between getting
information and purchasing. Therefore, we suggest
H1: Getting product information from a vendor’s website
positively influences purchasing a product from that
Web vendor.
To link behavioral intentions between getting information and
product purchasing, we refer to Gollwitzer’s (1999) theory of
implementation intentions, which are self-regulatory strategies
that
aim to drive a goal-oriented behavior. According to the theory,
a
goal-driven behavior automatically activates a set of goal-
enabling

(implementation) intentions that help realize the behavior
(Sheeran
and Orbell 1999). We view purchasing a specific product from
a
particular Web vendor as the goal behavior, while getting
information about the product from the Web vendor is viewed as
a
means to achieve the goal behavior (implementation intention).
Therefore, a goal intention to purchase a product from a Web
vendor
activates an intention to get information about that product from
the
vendor’s website.4 For example, a student that intends to buy a
textbook from Amazon is most likely to visit Amazon to get
price
information about the textbook. In terms of the temporal order,
consumers first form the intention to purchase a product to
fulfill a
particular need, and they then form the implementation
intentions to
facilitate fulfilling the need. Therefore, the product purchasing
(goal) intention precedes and drives the getting product
information
(implementation) intentions. Salisbury et al. (2001) show that
intentions to purchase relate to intention to get information.
The
preceding arguments suggest
H2: Intentions to purchase a product from a Web vendor
positively influence intentions to get information about
the product from the vendor’s website.
Attitude
Attitude has long been shown to influence behavioral intentions

(Ajzen and Fishbein 1980). This relationship has received sub-
stantial empirical support. With regard to the focal behaviors,
atti-
tude toward getting information and product purchasing is
defined
as the consumer’s evaluation of the desirability of using a
website
to get information and purchase products from a Web vendor,
respectively. Using a deductive logic, favorable attitude is
likely to
4Gollwitzer’s (1999) theory suggests that a goal behavior can
trigger several
implementation intentions. Intention to purchase a product
from a specific
Web vendor triggers intentions to get product information, not
only from the
specific vendor, but also from other sources. Both
implementation intentions
are potential consequences of the goal behavioral intention, but
the intention
to get information about a specific product from a specific Web
vendor is
more likely to occur, and it is thus examined.
encourage consumers to get information and purchase products
from
a vendor.
Subjective Norm

SN suggests that behavior is instigated by one’s desire to act as
important referent others act or think one should act. Applied
to the
two focal behaviors, SN reflects consumer perceptions of
whether
these two behaviors are accepted, encouraged, and implemented
by
the consumer’s circle of influence. The literature suggests a
positive
relationship between SN and intended behavior, and empirical
work
has shown that SN influences behavioral intentions toward
system
use (Karahanna et al. 1999). A positive relationship between
SN
and intentions to get information and purchase products from a
Web
vendor is thus expected.
Perceived Behavioral Control
PBC is a topic that has been debated in the social psychology
literature (for a review, see Trafimow et al. 2002). This paper
sheds
light on the nature and role of PBC by (1) clarifying its role in
TPB,
(2) describing its underlying dimensions, and (3) proposing a
parsimonious model that integrates its underlying dimensions
and
their antecedents into a coherent model.
The Role of PBC in TPB
PBC is defined as a person’s perception of how easy or difficult
it

would be to carry out a behavior (Ajzen 1991). To differentiate
PBC from attitude, Ajzen (2002b) emphasized that PBC denotes
a
subjective degree of control over the performance of a behavior
and
not the perceived likelihood that performing the behavior will
produce a given outcome. Ajzen suggested that PBC “should be
read as perceived control over the performance of a behavior”
(2002b, p. 668). Therefore, PBC is the consumer’s perceived
ease
or difficulty of getting product information from a vendor’s
website
and purchasing a product from a Web vendor, respectively.
In general, PBC plays a dual role in TPB. First, along with
attitude
and SN, it is a co-determinant of intention. Second, together
with
intention, it is a co-determinant of behavior. Support for the
role of
PBC on intention and behavior is provided by Mathieson (1991)
and
Taylor and Todd (1995b). We thus suggest
H3a: PBC over getting information from a Web vendor
positively influences (1) intention and (2) actual
behavior toward getting product information from
that Web vendor.
H3b: PBC over product purchasing from a Web vendor
positively influences (1) intention and (2) actual
behavior toward product purchasing from the Web
vendor.
Underlying Dimensions of PBC

Since the early days of TPB, there has been some ambiguity
surrounding the nature of PBC. Recently, questions regarding
its
nature and measurement have been attracting a lot of attention
(e.g.,
Ajzen 2002b; Trafimow et al. 2002). In particular, empirical
findings have cast doubt on Ajzen’s (1991) original assertion
that
PBC is a unitary construct, suggesting instead that PBC has two
distinct dimensions: self-efficacy (SE) and controllability.5
While
the conceptualization of SE and controllability is still
controversial,
there is an emerging consensus that the two are the underlying
dimensions of PBC. We offer the following definitions:
• Self-Efficacy: Following Bandura (1986), we define SE as
individual judgments of a person’s capabilities to perform a
behavior. Applied to e-commerce, SE describes consumers’
judgments of their own capabilities to get product information
and purchase products online.
• Controllability: We follow Ajzen (2002b) to define
controllability as individual judgments about the availability of
resources and opportunities to perform the behavior. Applied
to e-commerce, controllability describes consumers’ percep-
tions of whether getting information and purchasing products
online is completely up to them because of the availability of
resources and opportunities.
The Nature of Perceived Behavioral Control
Despite empirical evidence that SE and controllability can be
manipulated differently and can be reliably distinguished across
behaviors (e.g., Cheng and Chan 2000), Ajzen (2002b, p. 696)

maintains that “the fact that it is possible to reliably distinguish
between two different types of PBC—SE and controllability—
does
not invalidate the unitary nature of the [PBC] construct.” To
bridge
this inconsistency, he proposes a two-level hierarchical model
to
describe PBC as an “overarching, superordinate construct” (p.
697).
Hierarchical or higher-order models are used to explain the
interrelations among lower-order factors that constitute an inte-
grative latent construct. Higher-order models provide a more
coherent description of multiple facets of a complex
phenomenon
that could be described by a unitary factor (Law et al. 1998).
The
relationships between lower and higher order constructs can be
reflective or formative. While reflective structures assume that
the
5While the SE and controllability differ in their predictive
validity (e.g.,
Conner and Armitage 1998), there is no evidence to support the
common
view that SE reflects internal factors whereas controllability
reflects beliefs
about external factors (Ajzen 2002b).
PERCEIVED BEHAVIORAL CONTROLCONTROL BELIEFS

Attitude
Subjective
Norm
Perceived
Behavioral
Control
Intentions Behavior
Controllability
Self-Efficacy First-Order Construct
Second-Order Construct
Self-Efficacy
Beliefs
Controllability
Beliefs
Normative
Beliefs
Attitudinal
Beliefs
PERCEIVED BEHAVIORAL CONTROLCONTROL BELIEFS
Attitude
Subjective

Norm
Perceived
Behavioral
Control
Intentions Behavior
Controllability
Self-Efficacy First-Order Construct
Second-Order Construct
Self-Efficacy
Beliefs
Controllability
Beliefs
Normative
Beliefs
Attitudinal
Beliefs
Figure 2. The Proposed Extension of the Theory of Planned
Behavior
latent second order construct causes the first order factors,
formative
structures assume that the second order construct is caused by
the
first order factors (for a review, see Edwards 2001).

Figure 2 depicts our proposed extension of TPB with PBC
viewed
as a second-order factor formed by the first-order dimensions of
SE
and controllability.
The rationale for a formative model is based on the notion that
SE
and controllability are dynamic concepts (Bandura 1986), not
stable
traits. As dynamic concepts, they are likely to change over time
and
be manipulated differently by other factors (Trafimow et al.
2002).
Hence, PBC cannot equally cause SE and controllability, thus
rendering a reflective model unlikely. Moreover, since a
change in
one of the lower-order factors does not necessarily imply an
equal
change in the other, a formative model is deemed more likely.
In our endeavor to comprehensively predict the two key e-
commerce
behaviors, the proposed TPB extension allows for a thorough
prediction of PBC through its underlying dimensions and their
respective antecedents, while maintaining a parsimonious view
of
PBC. The following section elicits the antecedents of PBC
through
its two underlying dimensions, in addition to eliciting the
antecedent
beliefs of attitude.
Eliciting External Beliefs
TPB includes three categories of external beliefs: attitudinal,

normative, and control. These beliefs are scenario specific and
a
priori cannot be generalized. Hence, for each new behavior,
one
must identify five to nine salient beliefs for each behavior that
are
context and population specific (Ajzen and Fishbein 1980).
We conducted a belief elicitation study using an open-ended
questionnaire, following Ajzen’s (2002a) procedure. The aim
was
to freely elicit the most salient attitudinal and control beliefs,
which
correspond to specific open-ended questions (Table 1).
Normative
beliefs were not elicited since prior studies showed that SN has
a
weak role in online behaviors (George 2002). We solicited the
key
drivers for each behavior from a convenience sample of 56
participants, which included faculty, staff, and students of a
major
university in the United States. Their responses are sorted
based on
the frequency mentioned (Tables 2 and 3). We then chose the
beliefs that exceeded a 20 percent frequency cutoff, as
prescribed by
Ajzen and Fishbein (1980, p. 68) (presented in bold in the
tables).
The resulting set of beliefs span a wide range of characteristics,
which we grouped into six categories for better exposition: (1)
trust
in Web vendor, (2) technology acceptance, (3) consumer
resources,

(4) technological characteristics, (5) product characteristics,
and
(6) consumer skills. These categories were derived based on
literature grounding and practical empiricism. For getting
infor-
mation: (1) the attitudinal beliefs are trust, perceived
usefulness and
ease of use; (2) the controllability beliefs are trust, ease of use,
time
resources, download delay, and website navigability; and (3) the
SE
beliefs are ease of use and skills. For purchasing: (1) the
attitudinal
beliefs are trust, usefulness, ease of purchasing, and product
value;
(2) the controllability beliefs are trust, ease of purchasing,
monetary
resources, product diagnosticity, and information protection;
and
(3) the SE beliefs are ease of use and skills. Figure 3 depicts
our
proposed model.
TPB can aggregate beliefs to create measures of attitude, SN,
and
PBC (Ajzen and Fishbein 1980). This aggregation has been
criticized for not identifying specific factors that might predict
a
behavior (e.g., Taylor and Todd 1995a) and for the biases it
may
create (e.g., Karahanna et al. 1999). The idea that TPB beliefs
can
be decomposed into multidimensional constructs has been
credited
to Taylor and Todd (1995b), who introduced the decomposed
TPB

(DTPB). While we stay faithful to TPB, we decompose the
derived
beliefs following DTPB to provide a better understanding of
each
behavior. In doing so, we aim not only to assure high
explanatory
and predictive validity, but also to select managerially amenable
factors. We also use another variation of TPB to permit cross-
over
Table 1. Questionnaire for Eliciting External Salient Beliefs
Attitudinal
Beliefs
(Getting
Information)
1. Getting information about this particular product from this
vendor’s website in the next 30 days:
1a. What do you believe are the advantages of doing this?
1b. What do you believe are the disadvantages?
2. Anything else you associate with your getting information
about this product from this vendor’s
website?
Control
Beliefs

(Getting
Information)
3. What factors or circumstances would enable you to get
information about this product from this
vendor’s website?
4. What factors or circumstances would make it difficult for you
to get information about this product
from this vendor’s website?
5. Are there any other issues (barriers or facilitating conditions)
that come to mind when you think
about getting information about this product from this vendor’s
website?
Attitudinal
Beliefs
(Purchasing)
6. Purchasing the particular product from this Web vendor in
the next 30 days:
6a. What do you believe are the advantages of doing this?
6b. What do you believe are the disadvantages?
7. Anything else you associate with your purchasing this
product from this Web vendor?
Control
Beliefs
(Purchasing)
8. What factors or circumstances would enable you to purchase
this product from this Web vendor?

9. What factors or circumstances would make it difficult for you
to purchase this product from this Web
vendor?
10. Are there any other issues (barriers or facilitating
conditions) that come to mind when you think
about your purchasing this product from this Web vendor?
Table 2. Frequency of Elicited Beliefs (Getting Information)
Attitudinal Beliefs Frequency (%) Control Beliefs Frequency
(%)
Trust – Getting Information 37 (66%) Getting Information
Skills 31(55%)
Perceived Ease of Getting Info 33 (59%) Perceived Ease of
Getting Info 30 (54%)
Perceived Usefulness of Getting
Info
25 (45%) Trust – Getting Information 24 (43%)
Download Delay 14 (25%) Download Delay 21 (38%)
Perceived Risk of Getting Information 6 (11%) Time Resources
18 (32%)
Perceived Enjoyment 5 (8%) Website Navigability 12 (21%)
Product Variety 5 (8%) Website Features (e.g., search engine,
FAQ)
7 (13%)
Instant Gratification 2 (4%) Website Personalization 3 (5%)

EXTERNAL BELIEFS (PURCHASING)
EXTERNAL BELIEFS (GETTING INFO)
Trust - Getting Information
Download Delay
Trust – Product Purchasing
Product Diagnosticity
Getting Information Skills
Purchasing Skills
Website Navigability
Time Resources
PEOU of Purchasing
PU of Purchasing
Product Value
Information Protection
Monetary Resources

PU of Getting Info
PEOU of Getting Info
CONTROL VARIABLES
PERCEIVED BEHAVIORAL CONTROL
(GETTING INFORMATION)
Intention to
Get Info
Attitude toward
Getting Info
Intention to
Purchase
Controllability
(Getting Info)
Self-Efficacy
(Getting Info)
(PURCHASING)
Controllability
(Purchasing)
Attitude toward
Purchasing
Self-Efficacy
(Purchasing)

Subjective
on Getting
PBC
(Getting Info)
Getting Info
Behavior
Purchasing
Behavior
Subjective Norm
on Purchasing
PBC
(Purchasing) Past Experience
Habit
Web Vendor Reputation
Product Price
Consumer Demographics
EXTERNAL BELIEFS (PURCHASING)
EXTERNAL BELIEFS (GETTING INFO)
Trust - Getting Information
Download Delay
Trust – Product Purchasing

Getting Information Skills
Purchasing Skills
Time Resources
PEOU of Purchasing
PU of Purchasing
Product Value
Monetary Resources
PU of Getting Info
PEOU of Getting Info
CONTROL VARIABLES
(GETTING INFORMATION)
Intention to
Get Info
Attitude toward
Getting Info
Intention to
Purchase

Controllability
(Getting Info)
Self-Efficacy
(Getting Info)
(PURCHASING)
Controllability
(Purchasing)
Attitude toward
Purchasing
Self-Efficacy
(Purchasing)
Subjective
on Getting
PBC
(Getting Info)
Getting Info
Behavior
Purchasing
Behavior
Subjective Norm
on Purchasing
PBC
(Purchasing) Past Experience

Habit
Web Vendor Reputation
Product Price
Consumer Demographics
Table 3. Frequency of Elicited Beliefs (Purchasing)
Attitudinal Beliefs Frequency (%) Control Beliefs Frequency
(%)
Perceived Usefulness of
Purchasing
33 (59%) Monetary Resources 41 (73%)
Perceived Ease of Purchasing 32 (57%) Product Diagnosticity
33 (59%)
Trust – Purchasing 17 (30%) Perceived Ease of Purchasing 28
(57%)
Product Value 15 (27%) Product Value 25 (45%)
Monetary Resources 14 (25%) Trust - Purchasing 22 (39%)
Product Diagnosticity 13 (23%) Information Protection 18
(32%)
Product Quality 8 (14%) Purchasing Skills 12 (21%)
Perceived Risk of Purchasing 7 (13%) Delayed Gratification 3
(5%)
Product Variety 2 (4%) Quick Pay Availability (e.g., one-
click pay)
3 (5%)
Figure 3. The Proposed Research Model

effects between beliefs and perceptions (Taylor and Todd
1995a).
For example, trusting beliefs may simultaneously impact both
attitude and PBC for each behavior.
Trusting Beliefs
Trust has long been a central defining feature of economic and
social interactions where uncertainty, delegation of authority,
and
fears of opportunism are present (Luhmann 1979). Trust is the
belief that the trustee will act cooperatively to fulfill the
trustor’s
expectations without exploiting its vulnerabilities. A detailed
discussion on the nature and role of trust in e-commerce can be
found in Gefen et al. (2003), McKnight and Chervany (2002),
and
Pavlou (2003).
In general, trust is viewed as a three-dimensional construct,
composed of competence, integrity, and benevolence (Gefen et
al.
2003). Competence is the belief in the trustee’s ability to
perform
as expected by the trustor. Integrity is the belief that the trustee
will
be honest and keep its promises. Benevolence is the belief that
the
trustee will not act opportunistically, even given the chance. In
sum,

trust gives the trustor the confidence that the trustee will behave
capably (ability), ethically (integrity), and fairly
(benevolence).6
To be placed in a TPB-based model, trust must be defined with
respect to a behavior through a well-specified target, action,
context,
and time frame (Ajzen 2002a). The target of trust is the Web
ven-
dor, the action is getting information or purchasing, and the
context
is the online environment. In terms of time frame, the impact of
trust is observed for a specific window during which the
consumers
are making their decisions. This view is consistent with the
trust
literature where trust is considered with respect to a specific
trustor
(Mayer et al. 1995), context (Lewicki and Bunker 1995), and
time
window (Tan and Thoen 2001).
The practical utility of placing trust in the proposed TPB model
stems from the fact that Web vendors have a considerable
influence
on trust through their reputation and size (Jarvenpaa et al.
2000), and
institutional factors (Pavlou and Gefen 2004), among others.
Trusting Belief: Getting Information
Trust is important for getting information since consumers
assess
whether the information on a website is valid, credible, and
accurate
(Choudhury et al. 2001). Therefore, competence and integrity

are
the most relevant dimensions for getting information as they
reflect
the Web vendor’s ability to provide credible information. A
trusted
Web vendor eases fears that purposely false information may
expose
a consumer to adverse outcomes (Gefen 2002). In sum, trust for
getting information describes a consumer’s belief that the Web
vendor will provide valid, accurate, and timely information.
Trusting Belief: Product Purchasing
Trust is important for product purchasing since online
consumers are
vulnerable in several ways (e.g., not receiving the right product,
becoming victims of fraud). A trusted Web vendor must have
competence, integrity, and benevolence. Competence refers to
“the
expectation of technically competent role performance” (Barber
1983, p. 14). Integrity provides assurance that the vendor will
keep
promises. Benevolence ensures that the vendor will act fairly
and
stand behind its product, even if new conditions arise. In sum,
for
product purchasing, trust describes the belief that the vendor
will
properly deliver, fulfill, and stand behind its product.
Trusting Beliefs and Attitude
Trust is proposed as an attitudinal belief for both getting
information
and purchasing. The relationship between trust and attitude

draws
from the notion of perceived consequences (Triandis 1979).
Trust
enables favorable expectations that no harmful outcomes will
occur
if a trustor undertakes a behavior (Barber 1983). Trust also
refers to
optimistic expectations that the trustee will protect the trustor’s
interests (Hosmer 1995). In sum, trust creates favorable
perceptions
about the outcomes of the vendor’s actions, thus creating
positive
attitudes. In terms of getting information, trust creates positive
expectations that the vendor will post credible information. For
product purchasing, trust engenders confident expectations that
the
Web vendor will fulfill its promises. Using a similar logic,
Jarvenpaa et al. (2000), McKnight and Chervany (2002), and
Pavlou
(2003) show that trust has an impact on intentions by creating
positive attitudes. Therefore,
H4a: Trusting beliefs in a Web vendor regarding getting
information positively influence attitude toward
getting product information from that Web vendor.
H4b: Trusting beliefs in a Web vendor regarding product
purchasing positively influence attitude toward
product purchasing from the Web vendor.
Trusting Beliefs and Perceived Behavioral Control
Trust is also placed in the nomological structure of the TPB as a
control belief. The trust literature assumes that the trustor lacks
control over the trustee’s behavior, but trust builds the trustor’s
confidence to depend on the trustee (Fukuyama 1995). The

relationship between trust and PBC draws from Luhmann’s
(1979)
notion that trust reduces social uncertainty, which refers to all
6Trust has also been viewed as a four-dimensional construct,
comprising of
ability, integrity, benevolence, and predictability (McKnight
and Chervany
2002). However, the literature on buyer-seller relationships has
focused on
credibility (competence and integrity) and benevolence (Ba and
Pavlou 2002;
Doney and Cannon 1997). Therefore, predictability or
consistency is omitted.
unforeseen contingencies. In doing so, trust decreases efforts to
copiously account for all potential contingencies (Gefen 2002).
Following this logic, Zand (1972) concludes that by reducing
social
uncertainty, trust results in a greater controllability over the
behavior. Therefore, trust facilitates trusting behaviors, not by
controlling the Web vendor’s actions (such as in agency
theory), but
by overcoming psychological barriers to engaging in a behavior.
Trust thus acts as an uncertainty absorption resource that
enables the
trustor to better cope with social uncertainty. In terms of
getting
information, trust rules out negative contingencies due to the
information that the vendor provides on its website. In terms of

product purchasing, trust reduces the uncertainty of product
delivery
and fulfillment. We therefore propose the following
hypotheses:
H5a: Trusting beliefs in a Web vendor regarding getting
information positively influence controllability over
H5b: Trusting beliefs in a Web vendor regarding product
purchasing positively influence controllability over
product purchasing from that Web vendor.
TAM Beliefs
Following the TRA, TAM asserts that the intention to use a
system
is determined by two generalized beliefs: perceived usefulness
(PU)
and perceived ease of use (PEOU) (Davis 1989). The two TAM
variables have been used to predict Internet purchasing behavior
(e.g., Gefen et al. 2003; Koufaris 2002; Pavlou 2003).
Perceived Usefulness
PU is the extent to which one believes that using a system will
enhance her performance (Davis 1989). PU of getting
information
is defined as the extent to which a consumer believes that a
website
would enhance her effectiveness in getting product information.
Perceived purchasing usefulness is defined as the extent to
which a
consumer believes that a specific vendor would enhance her
effectiveness in purchasing products. PU has been shown to
influence behavioral intention through attitude (Davis1989;

Taylor
and Todd 1995b). Therefore, the following hypotheses are
proposed:
H6a: Perceived usefulness of getting information positively
influences attitude toward getting product information
from a Web vendor.
H6b: Perceived usefulness of product purchasing positively
influences attitude toward product purchasing from a
Web vendor.
Perceived Ease of Use
PEOU is the extent to which a person believes that using the
system
will be effortless (Davis 1989). Applied to online consumer
behavior, perceived ease of getting information is defined as the
extent to which a consumer believes that getting product
information
from a website would be free of effort. Similarly, perceived
ease of
purchasing is defined as the extent to which a consumer
believes that
purchasing products from a Web vendor would be free of effort.
Similar to PU, the role of PEOU on intentions is mediated by
attitude (Davis 1989; Taylor and Todd 1995b). Hence, we
propose
the following hypotheses:
H7a: Perceived ease of getting information positively
influences attitude toward getting product information
from a Web vendor.
H7b: Perceived ease of product purchasing positively
influences attitude toward product purchasing from a

Web vendor.
In addition to the attitudinal role of PEOU, the instrumental
aspect
of PEOU (Davis 1989) is viewed as a control belief that
facilitates
a behavior with lower personal effort (Lepper 1985). For
example,
Davis argued that SE is one of the means by which PEOU
influences
behavior. Applied to e-commerce, a website from which it is -
perceived as being easy to get information and make a purchase
is
likely to increase the consumer’s ability and confidence in
getting
information and purchasing, respectively.
Similarly, an easy to use website removes the cognitive
impediments
of using the website, making getting information and purchasing
more accessible to the consumer. It causes the perception of
these
online behaviors as being under the consumer’s full control,
thus
making getting product information and purchasing completely
up
to consumer. Thus, the following hypotheses are offered:
H8a: Perceived ease of getting information positively
influences (1) self-efficacy and (2) controllability over
H8b: Perceived ease of product purchasing positively
influence (1) self-efficacy and (2) controllability over
product purchasing from that Web vendor.

Consumer Resources
Time Resources
Leisure time has been considered a critical resource for getting
information (Bellman et al. 1999). Having the time needed to
browse for product information is a prerequisite for getting
information since time is a key resource for time-consuming
tasks.
We thus hypothesize that time resource is a facilitating
condition
that increases the controllability over a behavior.
H9a: Time resources positively influence controllability over
getting product information from a Web vendor.
Monetary Resources
Purchasing a product necessitates an outlay of monetary
resources.
Having the required monetary resources is a prerequisite for
purchasing a product. By overcoming the financial
impediments to
purchasing, consumers increase their controllability over
purchasing.
H9b: Monetary resources positively influence controllability
over product purchasing from a Web vendor.

Technological Characteristics
Download Delay
Download delay is defined as the amount of time it takes for a
website to display a requested page from a Web server (Rose et
al.
1999). Download delay relates to a website’s response time, a
factor
associated with lower intentions to use a system (Ives et al.
1983).
Download delay is also negatively related to the time needed to
perform a task, which has been shown to negatively impact
inten-
tions to use a system (Mawhinney and Lederer 1990).
Download
delay is thus expected to negatively impact attitude toward
getting
information since having to wait too long for information
creates
negative expectations about the behavior.
Rose et al. (1999) identified download delay as a key e-
commerce
barrier. Since download delay acts as an impediment to
receiving
information quickly, it reduces the availability of time resources
for
consumers, thus making it more difficult for them to get product
information. The preceding arguments suggest
H10a: Download delay negatively influences attitude toward
getting product information from a Web vendor.
H10b: Download delay negatively influences controllability
over product purchasing from a Web vendor.

Navigability is defined as the natural sequencing of web pages,
well-
organized layout, and consistency of navigation protocols
(Palmer
2002). A useful navigational structure facilitates traffic and
sales on
a Web site by increasing information availability (Lohse and
Spiller
1998). Navigability enables consumers to find the right set of
products and compare among alternatives. By making
information
easily accessible to consumers, navigability makes getting
informa-
tion be completely under the consumer’s control. We thus
propose
H11: Website navigability positively influences controlla-
bility over getting product information from a Web
vendor.
Concerns about information security and privacy have made
consumers skeptical about online transactions (George 2002),
and
they have been termed as key e-commerce obstacles (Hoffman
et al.
1999; Rose et al. 1999). Information security refers to the
consumers’ belief about the Web vendor’s ability to fulfill
security
requirements (e.g., authentication, encryption, and non-
repudiation)

(Cheung and Lee 2001). Information privacy refers to the
consumers’ belief about the Web vendor’s ability to protect
their
personal information from unauthorized use or disclosure
(Cassell
and Bickmore 2000). Information protection is defined as the
consumer’s belief about the Web vendor’s ability to safeguard
her
personal information from security and privacy breaches.7
When
consumers feel comfortable with the way a Web vendor will
protect
their personal information, they overcome any psychological
barriers to purchasing from that vendor. Thus,
H12: Information protection positively influences
controllability over product purchasing from a Web
vendor.
Product Characteristics
Product diagnosticity is the extent to which a consumer believes
that
a website is helpful in terms of fully evaluating a product
(Kempf
and Smith 1998). Product diagnosticity is driven by virtual and
functional control (Jiang and Benbasat 2004). Virtual control
refers
to allowing a consumer to manipulate a product image to see it
from
multiple angles and distances. Functional control allows a
consumer
to try different product functions. Since online consumers must
rely

on limited product representations (as opposed to traditional
commerce), by providing a real feel for the product and
enabling
adequate product evaluation, product diagnosticity overcomes
the
barrier created by the lack of physical inspection of products
and
7While information security and privacy can be viewed as
distinct constructs,
we propose a unitary view of information protection. The
unidimensionality
of information protection was validated during the pilot studies.
causes product purchasing to be under the consumer’s full
control.8
Accordingly, we propose
H13: Perceived diagnosticity positively influences con-
trollability over product purchasing from a Web
vendor.
Product Value
Product value refers to a product that offers an attractive com-
bination of quality and price. Price discounts are examples
where
the consumer can save money by getting a product at a lower
price,

and they have been shown to influence purchase intentions
(Alford
and Biswas 2002). Product value favorably predisposes
consumers
by allowing them to expect a high quality product at a low cost.
This suggests
H14: Product value positively influences attitude toward
product purchasing from a Web vendor.
Consumer Skills
An important prerequisite of engaging in a behavior is to have
the
necessary personal skills and knowledge to undertake the
behavior
(Koufaris 2002). Following Bandura (1986), SE is not
equivalent to
personal skills; SE deals with subjective judgments as to
whether
one has the personal skills needed to accomplish a behavior (p.
391). In contrast, “consumer skills” specifically describes the
knowledge and expertise a consumer has to undertake a
behavior,
and it is thus a potential predictor of whether a certain behavior
can
be accomplished.
Applied to e-commerce, getting information skills captures a
consumer’s knowledgeability in getting product information
from a
vendor’s website and making product evaluations. Having such
skills is likely to increase consumers’ judgments of how well
they
can get information from a vendor’s website, thus increasing
their

SE for getting information. Similarly, purchasing skills refer to
the
consumer’s knowledgeability about purchasing products online
and
making sound purchasing decisions, which are likely to increase
consumers’ judgments of their efficacy to purchase products
online,
leading to higher SE. We thus propose the following:
H15a: Getting information skills positively influence self-
efficacy over getting information from a Web vendor.
H15b: Purchasing skills positively influence self-efficacy over
product purchasing from a Web vendor.
Control Variables
The following variables are controlled for in this study:
• Past Experience: Studies have shown that past behavior
influences future behavior (Conner and Armitage 1988), and
online experience is a key factor in online behavior (Hoffman
et al. 1999). Hence, this study controls for the role of past
experience on both intentions and behaviors.
• Habit: Habit represents a variable that measures the frequency
of repeated performance of behavior, and it has been shown to
influence behavioral intentions (Limayem and Hirt 2003). In
e-commerce, Liang and Huang (1998) found that consumers’
prior experience had a moderating effect in predicting their
acceptance of Internet shopping (including the two behaviors
we consider). Therefore, the role of habit (both for getting
information and purchasing) is controlled for its impact on
getting information and purchasing, respectively.
• Web Vendor Reputation: The reputation of a Web vendor

has been shown to be an antecedent of transaction behavior
(Jarvenpaa et al. 2000), and it is thus controlled for in this
study.
• Product Price: Since both focal behaviors are based on a
specific product, product selection may differ across users and
lead to different degrees of uncertainty due to price (Ba and
Pavlou 2002). To account for product characteristics, we
control for product monetary price.
• Demographics: Finally, we also control for age, gender,
income, education, and Internet experience.
Research Methodology
Measurement Development
Following the TPB framework, each behavior must be defined
within a well-specified target, action, context, and time frame
(TACT) (Ajzen 2002a). Throughout the study, the target is the
Web
vendor, the action is either getting information or purchasing a
specific product, the context is the online environment, and the
time
frame is a specific window of time, set at 30 days after the
behavioral intentions were assessed.
All measurement items (Appendix A) were drawn from the
litera-
ture, and they were then adapted using standard psychometric
scale
development procedures (Boudreau et al. 2001) and a
refinement
8Product diagnosticity is not hypothesized to influence getting
information

since consumers can still get information about a product, but
they may not
purchase it online until they have fully evaluated the product in
a traditional
setting.
procedure based on the pilot studies. All scales followed
Ajzen’s
(2002a) recommendations for designing a TPB survey.
A single indicator (criterion variable) was used to assess PBC
(Taylor and Todd 1995b). The SE measures are based on
Compeau
and Higgins (1995). The controllability measures are based on
Taylor and Todd (1995b). Attitude and SN were adapted from
Karahanna et al. (1999).
In accordance with Ajzen and Fishbein’s (1980) expectancy–
value
formulation, belief-based measures are obtained by multiplying
belief strength and power (equations 1 through 3). Attitudinal
beliefs are measured as the product of behavioral belief strength
(b)
and outcome evaluation (e). Control beliefs are measured as the
product of control belief strength (c) and control belief power
(p).
Trust was based on McKnight and Chervany (2002). Trust
(getting
information) captures the vendor’s honesty and competence in

terms
of posting credible information. Trust (purchasing) captures the
Web vendor’s competence, integrity, and benevolence in
fulfilling
product orders. PU and PEOU were adapted from Gefen et al.
(2003). Time and monetary resources were based on Bellman et
al.
(1999), download delay on Rose et al. (1999), and website
navigability on Palmer (2002). Information protection was
based on
the scales of perceived privacy and security developed by
Cheung
and Lee (2001) and Salisbury et al. (2001). Product value was
based
on Chen and Dubinsky (2003), product diagnosticity on Jiang
and
Benbasat (2004), and consumer skills on Koufaris (2002).
Habit
was adapted from Limayem and Hirt (2003), and Web vendor
reputation from Jarvenpaa et al. (2000). Past behavior used
standard
items for past activities. Product price was ex post captured as
a
binary (high/low) variable.
Survey Administration
Following the development of the constructs and their opera-
tionalization, several small-scale pretests (including personal
interviews) were conducted with a total of 75 respondents to
enhance the psychometric properties of the measurement scales.
Given the large number of constructs in the proposed model, the
goal was to have a small number of items per construct while
retaining adequate measurement properties. Finally, a larger-
scale
pretest with 214 students was also contacted to confirm the

measurement properties of the final items and provide
preliminary
evidence for the proposed model. All pilot tests were conducted
following the same procedure as the subsequent actual data
collection (Churchill 1979).
This study’s main sample comprised 312 Internet consumers
drawn
from two populations. The first sample was selected from
students,
and the second sample consisted of Internet consumers. All
respondents were asked to click on the Web URL link provided
in
an invitation e-mail message, which linked to an online survey
instrument. The respondents were offered incentives in the
form of
a $250 draw and a report that summarized the study results.
The
invitees were assured that the results would be reported in
aggregate
to assure their anonymity.
Similar to the pilot studies, the respondents were asked to
choose a
specific product about which they were seriously considering
getting
information and purchasing online within the next 30 days.
Having
selected a product, they were then asked to select and report a
specific Web vendor that they had recently visited that offers
this
product. They were then asked to respond to the survey
questions
based on their selection. Thirty days after completing the first
survey, the respondents were contacted again. Following Blair

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